Sparse Convolutional Neural Networks

Sparse Convolutional Neural Networks . Baoyuan Liu1 , Min Wang1 , Hassan Foroosh1 , Marshall Tappen3 , and Marianna Penksy2. 1. Computational Imaging Lab, Computer Science, University of Central Florida, Orlando, FL, USA. 2. Department of Mathematics, University of Central Florida, Orlando, FL, USA. 3. , Seattle, WA 98109. {bliu, mwang, Abstract input feature maps input feature maps channel basis Deep Neural Networks have achieved remarkable per- formance in both image classification and object detection problems, at the cost of a large number of parameters and kernel computational complexity.}

convolutional kernel parameters of the network in [14] with relatively small number of bases while keeping the drop of accuracy to less than 1%. In our Sparse Convolutional Neural Networks (SCNN) model, each sparse convolutional layer can be performed with a few convolution kernels followed by a sparse ma-trix multiplication.

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  Network, Convolutional

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